Detecting Adversarial Audio via Activation Quantization Error

IJCNN 2020. Presented by Heng Liu

Transcript

1. Detecting Adversarial Audio via Activation Quantization Error Heng Liu and Gregory Ditzler Department of Electrical & Computer Engineering University of Arizona Tucson, AZ 85721 USA {hengl, ditzler}@email.arizona.edu
2. Outline • Introduction • Related works • Adversarial audio attack and detection • Neural network quantization • Contribution • Experiments • Conclusion and future works
3. Introduction: Insecure DNNs Adversarial images example
4. Introduction: Insecure DNNs Adversarial audio example
5. Introduction: Open problem How to detect? • Image applications • Image transformation • Feature transformation • Edge detection • Signal Processing based techniques • Quite effective • Audio applications • Techniques adopted from image domain • Limited security due to fundamentally different structure
6. Introduction: A separate but related topic Neural network quantization • Reduce DNN’s memory, and computational resource consumption • Deployment on fog and edge devices • Low latency application Contribution • DNN quantization is beneficial for adversarial detection • We propose to detect adversarial audios by using neural network quantization
7. Related works Adversarial audio attacks • Gradient based audio attack • Carlini and Wagner, 2018, SPW • Gradient based audio attack (over-the-air) • Yukura and Sakuma, IJCAI, 2019 • Black-box audio attack (free of gradient calculation) • Taori et al., SPW, 2019 Adversarial audio detection • Feature transformation • Frequency filters • Temporal dependency-based methods
8. Related works Neural network quantization • Quantization schemes • Weight quantization • Activation quantization • Quantization error • Accuracy loss comparing with full precision model ↵ <latexit sha1_base64="JtDqaCSYHdUsArJlViGZOYtHm8o=">AAAB7XicbVDLSgNBEOyNrxhfUY9eBoPgKeyKoMegF48RzAOSJfROJsmY2ZllZlYIS/7BiwdFvPo/3vwbJ8keNLGgoajqprsrSgQ31ve/vcLa+sbmVnG7tLO7t39QPjxqGpVqyhpUCaXbERomuGQNy61g7UQzjCPBWtH4dua3npg2XMkHO0lYGONQ8gGnaJ3U7KJIRtgrV/yqPwdZJUFOKpCj3it/dfuKpjGTlgo0phP4iQ0z1JZTwaalbmpYgnSMQ9ZxVGLMTJjNr52SM6f0yUBpV9KSufp7IsPYmEkcuc4Y7cgsezPxP6+T2sF1mHGZpJZJulg0SAWxisxeJ32uGbVi4ghSzd2thI5QI7UuoJILIVh+eZU0L6qBXw3uLyu1mzyOIpzAKZxDAFdQgzuoQwMoPMIzvMKbp7wX7937WLQWvHzmGP7A+/wBi4GPGA==</latexit> <latexit 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9. Contribution: Part 1 • Motivation: Error amplification effect • Adversarial perturbations are negligible at the input level, but are progressively amplified, eventually lead to wrong prediction • Effective on image defense techniques to ameliorate adversarial attacks • Hypothesis • We hypothesize that the activation quantization error on DNN’s output layer behaves differently for benign and adversarial audios Activation quantization errors on audios
10. Contribution: Part 1 • Victim model • DeepSpeech (open-sourced with pre-trained model), for ASR task • The benchmark dataset is Mozilla Common Voice • Adversarial audios • 1. Carlini and Wagner; • 2. Yukura and Sakuma; • 3. Taori et al. • Fixed width quantization • Variable activation quantization bit width for FCN and BiRNN layers • Bit quantization levels: 1 - 8 bits • Quantization error • Measured by Character Error Rate (CER): Calculated between transcripts from full precision and quantized models • CER is defined as: (S + D + I)/N FCN BiRNN FCN FCN FCN FCN Audios Transcription Activation Quantization Errors on Audios: Empirical analysis
11. Contribution: Part 1 Averaged CER: benign V.S. adversarial audios • Observations • The benign audios have an overall lower CER than all three types of adversarial audios • The differences vary across different quantization bit widths • Observation holds true for all three adversarial audio attacks
12. Contribution: Part 2 Adversarial audio detection method • Rule of Thumb: Classify audio clips that have a large activation quantization error as adversarial • How to determine the threshold and bit width? • We empirically estimate the best threshold and bit width Pseudo code
13. Experiments Detection against three audio attacks
14. Experiments: CER distribution ROC curve
15. Conclusion and future work • We examined the activation quantization error for benign and adversarial audios • We proposed an effective and reliable adversarial audio detection method Conclusions Future work • One future work is to analytically investigating the activation quantization error’s behavior Funding • This work was supported by grants from the Department of Energy #DE- NA0003946, Army Research Lab W56KGU-20-C-0002, and National Science Foundation CAREER #1943552
16. Questions Please email hengl@email.arizona.edu Thank You!